Please wait a minute...
Journal of Arid Land
Journal of Arid Land  2023, Vol. 15 Issue (12): 1439-1454    DOI: 10.1007/s40333-023-0035-2     CSTR: 32276.14.s40333-023-0035-2
Research article     
Analyzing environmental flow supply in the semi-arid area through integrating drought analysis and optimal operation of reservoir
Mahdi SEDIGHKIA(), Bithin DATTA
College of Science and Engineering, James Cook University, Townsville 4811, Australia
Download: HTML     PDF(1533KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

This study proposes a novel form of environmental reservoir operation through integrating environmental flow supply, drought analysis, and evolutionary optimization. This study demonstrates that simultaneous supply of downstream environmental flow of reservoir as well as water demand is challenging in the semi-arid area especially in dry years. In this study, water supply and environmental flow supply were 40% and 30% in the droughts, respectively. Moreover, mean errors of supplying water demand as well as environmental flow in dry years were 6 and 9 m3/s, respectively. Hence, these results highlight that ecological stresses of the downstream aquatic habitats as well as water supply loss are considerably escalated in dry years, which implies even using environmental optimal operation is not able to protect downstream aquatic habitats properly in the severe droughts. Moreover, available storage in reservoir will be remarkably reduced (averagely more than 30×106 m3 compared with optimal storage equal to 70×106 m3), which implies strategic storage of reservoir might be threatened. Among used evolutionary algorithms, particle swarm optimization (PSO) was selected as the best algorithm for solving the novel proposed objective function. The significance of this study is to propose a novel objective function to optimize reservoir operation in which environmental flow supply is directly addressed and integrated with drought analysis. This novel form of optimization system can overcome uncertainties of the conventional objective function due to considering environmental flow in the objective function as well as drought analysis in the context of reservoir operation especially applicable in semi-arid areas. The results indicate that using either other water resources for water supply or reducing water demand is the only solution for managing downstream ecological impacts of the river ecosystem. In other words, the results highlighted that replanning of water resources in the study area is necessary. Replacing the conventional optimization system for reservoir operation in the semi-arid area with proposed optimization system is recommendable to minimize the negotiations between stakeholders and environmental managers.

Key words： optimization      reservoir operation      droughts      metaheuristic algorithms      environmental flow regime     
Received: 01 August 2023      Published: 31 December 2023
Corresponding Authors: *Mahdi SEDIGHKIA (E-mail: sedighkia1365@gmail.com)
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Mahdi SEDIGHKIA
Bithin DATTA
Cite this article:

Mahdi SEDIGHKIA, Bithin DATTA. Analyzing environmental flow supply in the semi-arid area through integrating drought analysis and optimal operation of reservoir. Journal of Arid Land, 2023, 15(12): 1439-1454.

URL:

http://jal.xjegi.com/10.1007/s40333-023-0035-2     OR     http://jal.xjegi.com/Y2023/V15/I12/1439

Fig. 1 Workflow of this study. SDI, stream drought index.
Fig. 2 Location of the Latian Dam and upstream of the Jajrood River basin
Data Description
Hydrological data Historical recorded river flows (inflow) to reservoir for a long-term period (55 a) were available in the data bank of regional water authority
Evaporation data Average monthly evaporation data from the surface of reservoir was available based on long-term data recorded in the regional weather station near to reservoir
Environmental flow time series A recent regional technical report provided environmental flow analysis data (Abdoli and Sedighkia, 2019)
Water demand time series Available in the regional water authority dataset
Table 1 Introducing datasets
State Description Criterion of SDI
0 Non-drought SDI≥0.0
1 Mild drought -1.0≤SDI<0.0
2 Moderate drought -1.5≤SDI< -1.0
3 Severe drought -2.0≤SDI< -1.5
4 Extreme drought SDI< -2.0
Table 2 Criteria for definition of SDI
Fig. 3 Flowchart of particle swarm optimization (PSO)
Fig. 4 Flowchart of differential evolution (DE) algorithm
Fig. 5 Flowchart of biogeography-based optimization (BBO)
Fig. 6 Result of stream drought index (SDI)
Fig. 7 Time series of water demand, ideal environmental flow, the minimum environmental flow, and reservoir inflow
Fig. 8 Optimal release for water demand by different evolutionary algorithms. PSO, particle swarm optimization; BBO, biogeography-based optimization; DE, differential evolution algorithm. The abbreviations are the same in the following figures.
Fig. 9 Optimal release for environment by different evolutionary algorithms
displays the optimal storage in reservoir. Results demonstrate that performance of algorithms is different in term of storage in reservoir. Some points must be noted in optimal storage of reservoir. First, droughts might escalate the challenges of storage management. In fact, optimal storage is not accessible during dry years or reducing storage benefits is inevitable. However, results demonstrate that performance of the minimum storage penalty function was perfect. Storage is not less than the minimum operational storage in all time steps.
Fig. 10 Optimal storage in reservoir
Index PSO DE BBO
Reliability index of water supply (%) 38.80 38.60 39.40
Reliability index of environmental flow (%) 29.94 31.20 29.10
RMSE of water supply (m3/s) 6.80 6.80 6.90
RMSE of environmental flow (m3/s) 9.20 9.20 8.95
RMSE of storage (×106 m3) 34.60 37.30 36.70
Table 3 Measurement indices of reservoir operation optimization
[1]   Abdoli A, Sedighki M. 2019. Environmental Flow Assessment in Jajrood River Basin by Integrated Habitat Simulation Method Inspired from IFIM. Tehran: Department of Environment.
[2]   Ahmad A, El-Shafie A, Razali S F M, et al. 2014. Reservoir optimization in water resources: A review. Water Resources Management, 28(11): 3391-3405.
doi: 10.1007/s11269-014-0700-5
[3]   Akbari H, Rakhshandehroo G, Sharifloo A H, et al. 2015. Drought analysis based on standardized precipitation index (SPI) and streamflow drought index (SDI) in Chenar Rahdar River Basin, Southern Iran. American Society of Civil Engineers, 11-22.
[4]   Al-Jawad J Y, Tanyimboh T T. 2017. Reservoir operation using a robust evolutionary optimization algorithm. Journal of Environmental Management, 197: 275-286.
doi: S0301-4797(17)30297-9 pmid: 28391100
[5]   Allawi M F, Jaafar O, Hamzah F M, et al. 2018. Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models. Environmental Science and Pollution Research, 25(14): 13446-13469.
doi: 10.1007/s11356-018-1867-8
[6]   Altinbilek D. 2002. The role of dams in development. Water Science and Technology, 45(8): 169-180.
pmid: 12019817
[7]   Arunkumar R, Jothiprakash V. 2012. Optimal reservoir operation for hydropower generation using non-linear programming model. Journal of the Institution of Engineers (India), 93(2): 111-120.
[8]   Baba I, Midi H, Rana S, et al. 2015. An alternative approach of dual response surface optimization based on penalty function method. Mathematical Problems in Engineering, 2015: 450131, doi: 10.1155/2015/450131.
[9]   Bilgili M, Bilirgen H, Ozbek A, et al. 2018. The role of hydropower installations for sustainable energy development in Turkey and the world. Renewable Energy, 126: 755-764.
doi: 10.1016/j.renene.2018.03.089
[10]   Bovee K D, Lamb B L, Bartholow J M, et al. 1998. Stream habitat analysis using the instream flow incremental methodology. In: Biological Resources Division Information and Technology Report. U.S. Geological Survey. Colorado, USA.
[11]   Bozorg-Haddad O, Janbaz M, Loáiciga H A. 2016. Application of the gravity search algorithm to multi-reservoir operation optimization. Advances in Water Resources, 98: 173-185.
doi: 10.1016/j.advwatres.2016.11.001
[12]   Bozorg-Haddad O, Solgi M, Loáiciga H A. 2017. Meta-Heuristic and Evolutionary Algorithms for Engineering Optimization. New York: John Wiley & Sons.
[13]   Cai W, Zhang L, Zhu X, et al. 2013. Optimized reservoir operation to balance human and environmental requirements: A case study for the Three Gorges and Gezhouba Dams, Yangtze River Basin, China. Ecological Informatics, 18: 40-48.
doi: 10.1016/j.ecoinf.2013.06.009
[14]   Curry D M, Dagli C H. 2014. Computational complexity measures for many-objective optimization problems. Procedia Computer Science, 36: 185-191.
doi: 10.1016/j.procs.2014.09.077
[15]   Datta B, Burges S J. 1984. Short-term, single, multiple-purpose reservoir operation: Importance of loss functions and forecast errors. Water Resources Research, 20(9): 1167-1176.
doi: 10.1029/WR020i009p01167
[16]   Dehghanipour A H, Schoups G, Zahabiyoun B, et al. 2020. Meeting agricultural and environmental water demand in endorheic irrigated river basins: A simulation-optimization approach applied to the Urmia Lake basin in Iran. Agricultural Water Management, 241: 106353, doi: 10.1016/j.agwat.2020.106353.
[17]   Dhiman G, Singh K K, Slowik A, et al. 2021. EMoSOA: A new evolutionary multi-objective seagull optimization algorithm for global optimization. International Journal of Machine Learning and Cybernetics, 12: 571-596.
doi: 10.1007/s13042-020-01189-1
[18]   Dobson B, Wagener T, Pianosi F. 2019. An argument-driven classification and comparison of reservoir operation optimization methods. Advances in Water Resources, 128: 74-86.
doi: 10.1016/j.advwatres.2019.04.012
[19]   Duan W, He B, Nover D, et al. 2016. Floods and associated socioeconomic damages in China over the last century. Natural Hazards, 82: 401-413.
doi: 10.1007/s11069-016-2207-2
[20]   Duan W, Hanasaki N, Shiogama H, et al. 2019. Evaluation and future projection of Chinese precipitation extremes using large ensemble high-resolution climate simulations. Journal of Climate, 32(8): 2169-2183.
doi: 10.1175/JCLI-D-18-0465.1
[21]   Duan W, Zou S, Christidis N, et al. 2022. Changes in temporal inequality of precipitation extremes over China due to anthropogenic forcings. Npj Climate and Atmospheric Science, 5(1): 33, doi: 10.1038/s41612-022-00255-5.
[22]   Eberhart R, Kennedy J. 1995. Particle swarm optimization. In: 1995 IEEE international conference on neural networks proceedings, the University of Western Australia. Perth, Western Australia.
[23]   Ebtehaj I, Bonakdari H. 2016. Assessment of evolutionary algorithms in predicting non-deposition sediment transport. Urban Water Journal, 13(5): 499-510.
doi: 10.1080/1573062X.2014.994003
[24]   Ebtehaj I, Bonakdari H. 2017. Design of a fuzzy differential evolution algorithm to predict non-deposition sediment transport. Applied Water Science, 7(8): 4287-4299.
doi: 10.1007/s13201-017-0562-0
[25]   Ehteram M, Karami H, Mousavi S F, et al. 2017. Optimizing dam and reservoirs operation based model utilizing shark algorithm approach. Knowledge-Based Systems, 122: 26-38.
doi: 10.1016/j.knosys.2017.01.026
[26]   Ehteram M, Karami H, Mousavi S F, et al. 2018a. Reservoir operation by a new evolutionary algorithm: Kidney algorithm. Water Resources Management, 32(14): 4681-4706.
doi: 10.1007/s11269-018-2078-2
[27]   Ehteram M, Karami H, Mousavi S F, et al. 2018b. Evaluation of contemporary evolutionary algorithms for optimization in reservoir operation and water supply. Journal of Water Supply: Research and Technology-AQUA, 67(1): 54-67.
doi: 10.2166/aqua.2017.109
[28]   Ehteram M, Karami H, Farzin S. 2018c. Reducing irrigation deficiencies based optimizing model for multi-reservoir systems utilizing spider monkey algorithm. Water Resources Management, 32(7): 2315-2334.
doi: 10.1007/s11269-018-1931-7
[29]   Ebtehaj I, Bonakdari H, Es-haghi M S. 2019. Design of a hybrid ANFIS-PSO model to estimate sediment transport in open channels. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 43(4): 851-857.
doi: 10.1007/s40996-018-0218-9
[30]   García-Romero L, Paredes-Arquiola J, Solera A, et al. 2019. Optimization of the multi-start strategy of a direct-search algorithm for the calibration of rainfall-runoff models for water-resource assessment. Water, 11(9): 1876, doi: 10.3390/w11091876.
[31]   Hashimoto T, Stedinger J R, Loucks D P. 1982. Reliability, resiliency, and vulnerability criteria for water resource system performance evaluation. Water Resources Research, 18(1): 14-20.
doi: 10.1029/WR018i001p00014
[32]   Horne A, Kaur S, Szemis J, et al. 2017. Using optimization to develop a ''designer'' environmental flow regime. Environmental Modelling & Software, 88: 188-199.
[33]   Jowett I G. 1997. Instream flow methods: A comparison of approaches. Regulated Rivers: Research & Management, 13(2): 115-127.
[34]   Karami H, Farzin S, Jahangiri A, et al. 2019. Multi-reservoir system optimization based on hybrid gravitational algorithm to minimize water-supply deficiencies. Water Resources Management, 33(8): 2741-2760.
doi: 10.1007/s11269-019-02238-3
[35]   Karimi S S, Yasi M, Eslamian S. 2012. Use of hydrological methods for assessment of environmental flow in a river reach. International Journal of Environmental Science and Technology, 9(3): 549-558.
doi: 10.1007/s13762-012-0062-6
[36]   King J M, Tharme R E, De Villiers M S. 2000. Environmental Flow Assessments for Rivers: Manual for the Building Block Methodology. Pretoria: Water Research Commission.
[37]   Liersch S, Fournet S, Koch H, et al. 2019. Water resources planning in the Upper Niger River basin: Are there gaps between water demand and supply?. Journal of Hydrology, 21: 176-194.
[38]   Liu Y, Gupta H, Springer E, et al. 2008. Linking science with environmental decision making:Experiences from an integrated modeling approach to supporting sustainable water resources management. Environmental Modelling & Software, 23(7): 846-858.
[39]   Liu Y, Yang X, Ren L, et al. 2015. A new physically based self-calibrating Palmer drought severity index and its performance evaluation. Water Resources Management, 29(13): 4833-4847.
doi: 10.1007/s11269-015-1093-9
[40]   Liyanage C P, Yamada K. 2017. Impact of population growth on the water quality of natural water bodies. Sustainability, 9(8): 1405, doi: 10.3390/su9081405.
[41]   Nalamothu R. 2021. PHABSIM/SEFA application for minimum flows and levels development in Florida. Journal of Strategic Innovation & Sustainability, 16(1): 60-68.
[42]   Olden J D, Naiman R J. 2010. Incorporating thermal regimes into environmental flows assessments: Modifying dam operations to restore freshwater ecosystem integrity. Freshwater Biology, 55(1): 86-107.
doi: 10.1111/fwb.2009.55.issue-1
[43]   Paredes-Arquiola J, Solera A, Martinez-Capel F, et al. 2014. Integrating water management, habitat modelling and water quality at the basin scale and environmental flow assessment: Case study of the Tormes River, Spain. Hydrological Sciences Journal, 59(3-4): 878-889.
doi: 10.1080/02626667.2013.821573
[44]   Postel S L. 1998. Water for food production: Will there be enough in 2025?. BioScience, 48(8): 629-637.
doi: 10.2307/1313422
[45]   Qasem S N, Ebtehaj I, Bonakdari H. 2017. Potential of radial basis function network with particle swarm optimization for prediction of sediment transport at the limit of deposition in a clean pipe. Sustainable Water Resources Management, 3(4): 391-401.
doi: 10.1007/s40899-017-0104-9
[46]   Qin A K, Huang V L, Suganthan P N. 2008. Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE transactions on Evolutionary Computation, 13(2): 398-417.
doi: 10.1109/TEVC.2008.927706
[47]   Rezaee Zaman M, Morid S, Delavar M. 2013. Evaluate the effects of climate change on hydro-climatologic variables in Siminerood Basin. Journal of Water and Soil, 27(6): 1247-1259.
[48]   Salazar J Z, Reed P M, Quinn J D, et al. 2017. Balancing exploration, uncertainty and computational demands in many objective reservoir optimization. Advances in Water Resources, 109: 196-210.
doi: 10.1016/j.advwatres.2017.09.014
[49]   Sedighkia M, Ayyoubzadeh S A, Hajiesmaeli M. 2017. Modification of Tennant and wetted perimeter methods in Simindasht Basin, Tehran Province. Civil Engineering Infrastructures Journal, 50(2): 221-231.
[50]   Sedighkia M, Datta B, Abdoli A. 2021. Minimizing physical habitat impacts at downstream of diversion dams by a multiobjective optimization of environmental flow regime. Environmental Modelling & Software, 140: 105029, doi: 10.1016/j.envsoft.2021.105029.
[51]   Shafroth P B, Schlatter K J, Gomez-Sapiens M, et al. 2017. A large-scale environmental flow experiment for riparian restoration in the Colorado River Delta. Ecological Engineering, 106: 645-660.
doi: 10.1016/j.ecoleng.2017.02.016
[52]   Shokoohi A, Amini M. 2014. Introducing a new method to determine rivers' ecological water requirement in comparison with hydrological and hydraulic methods. International Journal of Environmental Science and Technology, 11(3): 747-756.
doi: 10.1007/s13762-013-0404-z
[53]   Simon D. 2008. Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 12(6): 702-713.
doi: 10.1109/TEVC.2008.919004
[54]   Spiliotis M, Mediero L, Garrote L. 2016. Optimization of hedging rules for reservoir operation during droughts based on particle swarm optimization. Water Resources Management, 30(15): 5759-5778.
doi: 10.1007/s11269-016-1285-y
[55]   Sun S, Cao Z, Zhu H, et al. 2019. A survey of optimization methods from a machine learning perspective. IEEE Transactions on Cybernetics, 50(8): 3668-3681.
doi: 10.1109/TCYB.6221036
[56]   Takada A, Hiramatsu K, Trieu N A, et al. 2019. Development of an optimizing method for the operation rule curves of a multipurpose reservoir in a Southeast Asian watershed. Paddy and Water Environment, 17(2): 195-202.
doi: 10.1007/s10333-019-00711-8
[57]   Tennant D L. 1976. Instream flow regimens for fish, wildlife, recreation and related environmental resources. Fisheries, 1(4): 6-10.
doi: 10.1577/1548-8446(1976)001&lt;0006:IFRFFW&gt;2.0.CO;2
[58]   Tharme R E. 2003. A global perspective on environmental flow assessment: Emerging trends in the development and application of environmental flow methodologies for rivers. River Research and Applications, 19(5-6): 397-441.
doi: 10.1002/rra.v19:5/6
[59]   Yarnell S M, Stein E D, Webb J A, et al. 2020. A functional flows approach to selecting ecologically relevant flow metrics for environmental flow applications. River Research and Applications, 36(2): 318-324.
doi: 10.1002/rra.v36.2
[60]   Yaseen Z M, Allawi M F, Karami H, et al. 2019. A hybrid bat-swarm algorithm for optimizing dam and reservoir operation. Neural Computing and Applications, 31(12): 8807-8821.
doi: 10.1007/s00521-018-3952-9
[61]   Yaseen Z M, Karami H, Ehteram M, et al. 2018. Optimization of reservoir operation using new hybrid algorithm. Journal of Civil Engineering, 22(11): 4668-4680.
[62]   Yin X A, Yang Z F, Petts G E. 2012. Optimizing environmental flows below dams. River Research and Applications, 28(6): 703-716.
doi: 10.1002/rra.v28.6
[63]   Young W J, Scott A C, Cuddy S M, et al. 2003. Murray flow assessment tool:A technical description. In: Report to the CRC for Freshwater Ecology. CSIRO Land and Water, Canberra, Australia.
[64]   Zamani R, Akhond-Ali A M, Ahmadianfar I, et al. 2017. Optimal reservoir operation under climate change based on a probabilistic approach. Journal of Hydrologic Engineering, 22(10): 05017019, doi: 10.1061/(ASCE)HE.1943-5584.0001559.
[65]   Zarei A, Mousavi S F, Gordji M E, et al. 2019. Optimal reservoir operation using bat and particle swarm algorithm and game theory based on optimal water allocation among consumers. Water Resources Management, 33(9): 3071-3093.
doi: 10.1007/s11269-019-02286-9
[66]   Zeiringer B, Seliger C, Greimel F, et al. 2018. River Hydrology, Flow Alteration, and Environmental Flow in Riverine Ecosystem Management. Cham: Springer.
[67]   Zhao T, Zhao J, Yang D. 2014. Improved dynamic programming for hydropower reservoir operation. Journal of Water Resources Planning and Management, 140(3): 365-374.
doi: 10.1061/(ASCE)WR.1943-5452.0000343
[1] Olfa TERWAYET BAYOULI, ZHANG Wanchang, Houssem TERWAYET BAYOULI. Combining RUSLE model and the vegetation health index to unravel the relationship between soil erosion and droughts in southeastern Tunisia[J]. Journal of Arid Land, 2023, 15(11): 1269-1289.
[2] LIN En, LIU Hongguang, LI Xinxin, LI Ling, Sumera ANWAR. Promoting the production of salinized cotton field by optimizing water and nitrogen use efficiency under drip irrigation[J]. Journal of Arid Land, 2021, 13(7): 699-716.
[3] DUN Hongchao, HUANG Ning, ZHANG Jie. Optimization designs of artificial facilities in deserts based on computational simulation[J]. Journal of Arid Land, 2021, 13(3): 290-302.
[4] Mehdi GHOLAMI ROSTAM, Seyyed Javad SADATINEJAD, Arash MALEKIAN. Precipitation forecasting by large-scale climate indices and machine learning techniques[J]. Journal of Arid Land, 2020, 12(5): 854-864.
[5] Qiang LI, Xi CHEN, Yi LUO, ZhongHua LU, YanGang WANG. A new parallel framework of distributed SWAT calibration[J]. Journal of Arid Land, 2015, 7(1): 122-131.
[6] SongHao SHANG. Lake surface area method to define minimum ecological lake level from level–area–storage curves[J]. Journal of Arid Land, 2013, 5(2): 133-142.
[7] Yue HUANG, Xi CHEN, YongPing LI, AnMing BAO, YongGang MA. A simulation-based two-stage interval-stochastic programming model for water resources management in Kaidu-Konqi watershed, China[J]. Journal of Arid Land, 2012, 4(4): 390-398.